Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method  

作　　者：Mengjie Han Fatemeh Johari Pei Huang Xingxing Zhang 

机构地区：[1]School of Information and Engineering,Dalarna University,Falun 79188,Sweden [2]Built Environment Energy Systems Group,Department of Civil and Industrial Engineering,Uppsala University,Uppsala 75121,Sweden

出　　处：《Energy and Built Environment》2023年第4期418-431,共14页能源与人工环境（英文）

基　　金：The authors are thankful for the financial support from the UBMEM project from the Swedish Energy Agency(Grant No.46068).

摘　　要：Household electricity demand has substantial impacts on local grid operation,energy storage and the energy per-formance of buildings.Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management.However,such type of data is often expensive and time-consuming to collect,process and integrate.Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process.Incomplete data due to confiden-tiality concerns or system failure can further increase the difficulty of modeling and optimization.In addition,methods using historical data to make predictions can largely vary depending on data quality,local building envi-ronment,and dynamic factors.Considering these challenges,this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recom-bining them into synthetics.The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics.A reference building was used to provide empirical parameter settings and validations for the studied buildings.An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method.The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data.The average monthly error for the best month reached 15.9%and the best one was below 10%among 11 tested months.Less than 0.6%improper synthetic values were found in the studied region.

关 键 词：Data generation Time series decomposition Hourly electricity demand Large-scale buildings 

分 类 号：TU20[建筑科学—建筑设计及理论]